TSMixer
Summary
TSMixer is an all-MLP time-series forecasting architecture that mixes information along time and feature dimensions without self-attention. In this wiki it is mainly important as the compact architectural ancestor of Tiny Time Mixers.
Role In The Wiki
TSMixer is a useful counterweight to Transformer-first assumptions in time-series modeling. It suggests that strong time-step-dependent and feature-mixing priors can be competitive for forecasting, especially when cross-variate and exogenous information are available.
Official Artifacts
- Original paper code: https://github.com/google-research/google-research/tree/master/tsmixer
- PyTorch implementation: https://github.com/ditschuk/pytorch-tsmixer
Evidence
Relation To Foundation TSFM Agenda
Use the source-level agenda mappings rather than duplicating verdict rows here:
At the entity level, TSMixer is a useful counterweight to Transformer-first assumptions in time-series modeling. It suggests that strong time-step-dependent and feature-mixing priors can be competitive for forecasting, especially when cross-variate and exogenous information are available. This page should stay as the object card; source pages carry slot-level verdicts, evidence, and missing pieces.